class_docstr: Lag correlation (ACF/PACF) plots, commonly known as lollipop, for analysing correlation on
  a given lag. Useful for auto-, partial-, cross- and partial-cross-correlation.
class_docstr_tok: ["Lag","correlation","(","ACF\/PACF",")","plots",",","commonly","known","as","lollipop",",","for","analysing","correlation","on","a","given","lag",".","Useful","for","auto-",",","partial-",",","cross-","and","partial-cross-correlation","."]
code: def _plot(self) -> plt.figure:
      fig = plt.figure(figsize=self.figsize)
      ax = fig.add_subplot(1, 1, 1)
  
      lag = self.df[self.col_lag]
      corr = self.df[self.col_correlation]
      up = self.df[self.col_up]
      low = self.df[self.col_low]
  
      ax.vlines(lag, [0], corr, color="0.1")
      ax.axhline(color="0")
      ax.plot(
          lag,
          corr,
          marker="o",
          markersize=4,
          linestyle="None",
          color=self.PALETTE[0],
      )
      ax.fill_between(lag, up, low, alpha=0.25, color=self.PALETTE[0])
      if self.title:
          ax.set_title(self.title)
      ax.spines["top"].set_visible(False)
      ax.spines["right"].set_visible(False)
  
      plt.tight_layout()
      plt.close(fig)
      return fig
  
file_path: tslumen/tslumen/plot/static/correlation.py
imports: from typing import Tuple, Optional
  import warnings
  from dataclasses import dataclass
  import numpy
  import pandas
  from scipy.stats import gaussian_kde
  import matplotlib
  import matplotlib.pyplot
  from tslumen.plot.static.base import Figure
label_desc: Use image node_id 1 for calling the LagCorrelation obj's underlying member method code with example usage: obj._plot() and returns: fig